Editorial anatomical illustration of a person, with brain, lungs, heart and nervous system visible
01 / COGNITIONThink. Perceive. Remember.
02 / EXPERIENCEFeel. Sense. Interpret.
03 / BODYRhythms. Signals. Change.
04 / CONTEXTDecide. Act. Relate.
THE PERSON IS THE STARTING POINT.

COGNITIVE INTELLIGENCE DEVELOPMENT

Intelligence starts
with the human.

How does a person feel, think, decide and act?
We start there. Then we design the data and the intelligence.

COGNITIVE SCIENCE → HUMAN MEASUREMENT → APPLIED AILED BY BENAN D. ALLAN, PhD

THE HUMAN → INTELLIGENCE JOURNEY

01 / HUMAN UNDERSTANDING

Start with
the person.

What is changing within this person, over time?

Define the experience and the question. History, context and the person’s own account give the signals meaning.

02 / OBSERVATION

Choose what
to observe.

Signals are selected because they might help answer the human question.

Trackers, tasks and self-reports provide different views. We decide what to collect, when, and under which conditions.

03 / MEASUREMENT & ANALYSIS

Give the data
meaning.

An observation becomes a useful variable through definition and validation.

Keep context attached. Compare patterns, account for missing information, and test what the measurement can support.

04 / SYSTEM INTELLIGENCE

Build around
the evidence.

Contextual insights that account for a person’s baseline.

Translate the scientific logic into a product with engineering. Keep uncertainty visible and evaluate the experience in use.

SCROLL TO FOLLOW THE STORY ↓

01 / THE APPROACH

Before there is data,
there is a person.

A pause can be hesitation, distraction or careful thought. A faster heartbeat can follow effort, excitement or stress. The meaning depends on the person and the situation.

Our work connects cognitive science with system design: define the human question, decide what could represent it, test the measurement, and build around what the evidence supports.

The method travels across domains. The variables, models and decisions are designed for each system.

01

Understand

The person, the experience and the question worth asking.

Human phenomenon
02

Define

Give the concept a measurable meaning in its context.

Measurement framework
03

Observe

Choose signals, trackers, tasks and self-reports.

Contextual observations
04

Represent

Create variables that preserve the meaning of the signal.

Human variables
05

Analyze

Test patterns, uncertainty, validity and predictive value.

Supported evidence
06

Build

Work with engineering to create useful, bounded intelligence.

System intelligence

↳ EVALUATE IN USE. RETURN TO THE HUMAN QUESTION. REFINE.

02 / EXPLORE HUMAN DATA

What makes us human.
What we can measure.

Follow a human question through the scientific and computational workflow.

CHOOSE A DIMENSION

Illustrative measurement designs.
Each project requires its own validation.

HUMAN → DATA → INTELLIGENCECognition

Thinking & attention

What helps someone focus, understand and learn?

HUMAN QUESTION
Attention, memory, perception and cognitive effort during a specific task.
OBSERVATIONS
Task responses, eye movements, response times and self-reported effort; EEG or fMRI in a suitable research setting.
VARIABLES
Response latency, error patterns, gaze allocation and task-specific effort ratings.
ANALYSIS
Compare task conditions, check reliability and test how indicators relate to performance.
INTELLIGENCE
Adapt pacing, explanation or task difficulty to evidence about the learner.

Gaze duration or a brain signal does not directly reveal attention or understanding. Interpretation requires context and validation.

WAYS OF OBSERVING

Self-reports & interviews · Digital behavior · Wearables & trackers · Speech & interaction · Physiological signals · EEG, fMRI & eye-tracking

03 / INTELLIGENCE FOR EACH SYSTEM

The same signal.
A different question.

A system becomes useful when its measurements, model and response fit the person and the purpose.

↝

Health & wellbeing

Combine longitudinal physiological signals with symptoms and lived experience.

FROM → TO

Temperature + cycle history
→ contextual prediction

↗

Learning & coaching

Understand attention, preference and engagement within a learning journey.

FROM → TO

Interaction + feedback
→ adaptive recommendations

↔

Everyday decisions

Connect goals, constraints and behavior to support a useful next step.

FROM → TO

Intent + patterns + context
→ personalized support

Context stays attached. A variable needs a definition, a source and a reason to exist.

Uncertainty stays visible. Associations and estimates have limits.

People keep agency. The system supports decisions and respects consent.

04 / APPLIED WORK

From scientific questions
to products people use.

WOMEN’S HEALTH / Nar

Understanding change
within a person.

Biomarkers, symptoms and hormonal context inform personalized health insights. Individual baselines help give change meaning.

Women’s health intelligence
FAMILIES / TinyTaste

Turning a food log
into useful guidance.

A child’s age and nutritional needs shape how food entries are represented and how next-food suggestions are designed.

Explore TinyTaste ↗
GOALS / AIM

Making progress
personal.

Goal structure, behavior and feedback inform plans and check-ins that adapt to the person’s journey.

Explore AIM ↗
CONVERSATIONS / Prism

Giving a transcript
more context.

Cognitive frameworks organize conversational evidence into indicators of intent, uncertainty and decision patterns.

Private beta · conversation intelligence

PREVIOUS SCIENTIFIC LEADERSHIPStardust · CoachHub · Humaine AI · H-Farm

Full portfolio ↗

05 / WORK TOGETHER

Bring the human question.
We’ll build from there.

For founders and teams developing intelligent products, with particular experience in women’s health, families and learning.

01

Scientific consultation

Clarify the question, the signals to collect and what your system can responsibly infer.

02

Measurement to intelligent product

Design variables, analysis and predictive logic, then take the scientific requirements into engineering and delivery.

Discuss a project ↗
03

Embedded science & AI leadership

Scientific direction, model review and interdisciplinary leadership as your product develops.

Work together ↗

06 / THE SCIENTIST BEHIND CID

Benan D. Allan, PhD

Cognitive Scientist | Human-Centered AI Leader

My work begins with how humans experience the world. I define what should be measured, create variables that preserve the meaning of a human signal, and work with engineers to turn evidence into intelligent products.

Across 11+ years, I have worked in cognitive science, behavioral research, women’s health AI, personalization and immersive technology. My PhD in Brain, Mind and Computer Science was at the University of Padova, with visiting research at the University of Edinburgh.

You work directly with me. When a project needs additional specialists, we build the right team around it.

More about Benan ↗

LET’S START WITH YOUR QUESTION

What are you trying
to understand?

benan@cogdev.ai ↗

A conversation about your people, your product and what the intelligence needs to do.